Random Forest: Machine Learning Interview Prep 10
Random forest is like a group decision-making process where many “trees” or opinions come together to make a final decision. Each tree, or “classifier,” votes on the outcome, and the most popular choice wins. It’s like asking multiple experts for their opinions and going with the most common answer. This method is powerful because it reduces the chance of errors and overfitting, making it a popular choice for various Machine Learning tasks.
Let’s check your basic knowledge of Random Forest and help you prepare for your upcoming Data Scientist or Machine Learning Engineer interviews. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!
Question 1: Which one is correct?
(A) A Random Forest is a type of ensemble learning algorithm that combines multiple decision trees to make more accurate predictions.
(B) Random Forests are known for their high accuracy and are among the best-performing algorithms for many classification and regression tasks.
(C) Random Forests are less sensitive to noise and outliers in the data compared to other algorithms.
(D) All of the above
Question 2: Which one is correct?
(A) Random Forests can handle large datasets with many features and can be trained efficiently on parallel and distributed systems.
(B) Random Forests provide feature importance scores that can be used to understand the relative importance of each feature in the prediction.
(C) Random Forest algorithm is available on Scikit Learn and TensorFlow
(D) All of the above
Question 3: What are the advantages of using a Random Forest?
(A) Robustness
(B) Scalability
(C) Interpretability
(D) All of the above
Question 4: How does a Random Forest prevent overfitting? (select two)
(A) Random feature selection involves randomly selecting a subset of features to split each node of the decision tree, which helps to reduce the correlation between the trees and improve the diversity of the forest.
(B) Bagging involves training multiple decision trees on random subsets of the data and then averaging their predictions to obtain the final prediction, which helps to reduce the variance and improve the generalization performance of the forest.
(C ) Random feature selection involves randomly selecting a subset of features to split each node of the decision tree, which helps to increase the correlation between the trees and improve the diversity of the forest.
(D) Bagging involves training multiple decision trees on random subsets of the data and then averaging their predictions to obtain the final prediction, which helps to increase the variance and improve the generalization performance of the forest.
Question 5: How do you interpret the feature importance scores in a Random Forest?
(A) Higher scores indicate that the feature is more important, and lower scores indicate that the feature is less important.
(B) The scores are typically calculated based on the reduction in the impurity of the node when the feature is used to split the data.
(C) Feature importance scores can be used to select the most relevant features for the prediction or to gain insights into the underlying patterns in the data.
(D) All of the above
Question 6: What are the advantages of using a Random Forest over a single decision tree?
(A) Random Forests are less prone to overfitting than single decision trees because they combine the predictions of multiple trees, which helps to reduce the variance and improve the generalization performance of the model.
(B) Random Forests are typically more accurate than single decision trees because they capture more of the underlying patterns in the data by combining the predictions of multiple trees.
(C) Random Forests are more robust to noise and outliers in the data because they average the predictions of multiple trees, which helps to reduce the impact of individual noisy or outlier data points.
(D) All of the above
Question 7: What is the difference between Random Forest and XGBoost?
(A) Random Forest builds each tree independently, whereas XGBoost builds each tree sequentially and tries to correct the mistakes of the previous tree. This means that XGBoost can often achieve higher accuracy than Random Forest for complex datasets.
(B) XGBoost uses a gradient boosting approach, which means that it tries to optimize a loss function by iteratively adding decision trees that minimize the loss. Random Forest, on the other hand, uses a bagging approach, which means that it randomly samples the data and features to create multiple trees that are then combined to make a prediction.
(C) XGBoost is computationally more expensive than Random Forest, especially for large datasets, due to the sequential nature of the algorithm. Random Forest, on the other hand, can be parallelized and scaled more easily.
(D) All of the above
Question 8: What is the difference between Random Forest and Gradient Boosting?
(A) Random Forest is a group of trees, which will later be averaged or majority voted on. Each Decision Tree is independent. Thus the name Random Forest. This method is also called bagging. Gradient Boosting Decision Tree is a sequence of trees, where each tree is built based on the results of previous trees. So trees are not independent. This method is called boosting.
(B) In Random Forest, each tree is DEEP. Each tree is trained on a sufficient amount of data (from a random subset of the whole training set) using a random subset of features, and the tree is developed as a big tree. In Gradient Boosting, each tree is SMALL. We call it weak learners. It is weak in a way that the tree is very shallow, like 4 layers or even less. Each weak learner is supposed to pick only a small amount of signal, and every time we are making a small progress. By having many steps of learning, we can also get a robust performance.
(C) In a Random Forest, each Decision Tree has low bias and thus high variance. By averaging them together, we can achieve a much lower variance by compromising a tiny bit of bias. In Gradient Boosting, each Decision Tree has high bias and low variance. So by combining them together sequentially, we can keep the low variance but also get a low bias.
(D) All of the above
Question 9: Which statements are correct about Bagging and Bootstrapping? (select two)
(A) Bagging is a parallel ensemble meta-algorithm and a combination of Bootstrap and Aggregation which is developed to increase variance and improve the accuracy of ML problems that can be used in both Regression and Classification.
(B) Bootstrapping is a technique used to generate samples by randomly choosing data points without replacements from the original dataset. These samples are called Bootstrap samples whose size is less than the original dataset.
(C) Bagging is a parallel ensemble meta-algorithm and a combination of Bootstrap and Aggregation which is developed to reduce variance and improve the accuracy of ML problems that can be used in both Regression and Classification.
(D) Bootstrapping is a technique used to generate samples by randomly choosing data points with replacements from the original dataset. These samples are called Bootstrap samples whose size is less than the original dataset.
Question 10: Which statement is correct about bagging?
(A) Many weak learners aggregated typically outperform a single learner over the entire set and have less overfit.
(B) Reduces variance in high-variance datasets.
(C) Parallel processing for both training and evaluation.
(D) All of the above
The solutions will be published in the next quiz K-Nearest Neighbors (KNN): Machine Learning Interview 11.
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The solution of the previous quiz, Regularization: Machine Learning Interview Prep 09 - 1(A, B), 2(A, B), 3(D), 4(D), 5(C), 6(A), 7(A), 8(A), 9(D), 10(D).
References:
[1] Interview Questions for Random Forest, https://medium.com/@thedatabeast/interview-questions-for-random-forest-2bf784a6d0b1
[2] In Interview: How to answer “Compare Random Forest and Gradient Boosting Decision Tree”? https://medium.com/@penggongting/in-interview-how-to-answer-compare-random-forest-and-gradient-boosting-decision-tree-105de35cff3b
[3] Everything about Ensemble Learning, https://medium.com/geekculture/everything-about-ensemble-learning-46cd96d5498e
[4] StatQuest: Random Forests Part 1 — Building, Using and Evaluating, https://www.youtube.com/watch?v=J4Wdy0Wc_xQ&t=29s&ab_channel=StatQuestwithJoshStarmer
[5] StatQuest: Random Forests Part 2: Missing data and clustering, https://www.youtube.com/watch?v=sQ870aTKqiM&ab_channel=StatQuestwithJoshStarmer
